Asssignment2 of 615
Clare Tang
2021/10/7
Background
Data is downloaded from website of gapminder, seehttps://www.gapminder.org/data/.
Variables chosen
Life expectancy.
The average number of years a newborn child would live if current mortality patterns were to stay the same.Government health spending per person.
The average health expenditure per person that has been paid by government entities during the given year, expressed in US dollars using the average exchange rate.
Goals
- Make data description.
- Tidy the datasets.
- Figure out the changes of spending and life expectancy in different countries.
Data exploration
Raw data
1.Government health spending per person
- First, let us take a look at the spending data. It has spending data of 192 countries and includes data across 17 years from 1994 to 2009.
country 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Afghanistan NA NA NA NA NA NA NA 0.833 1.25 1.61 2.52 2.81 3.5 3.75 3.91 4.39 Angola 13.5 8.72 10.5 6.65 6.55 12.5 18.3 14.300 19.40 19.70 27.10 50.90 68.4 126.00 181.00 102.00 Albania 13.9 17.10 14.2 18.60 28.10 27.2 30.5 32.500 40.60 63.90 71.40 75.50 88.8 109.00 107.00 94.00 Andorra 897.0 983.00 967.0 1340.00 942.00 862.0 890.0 1020.000 1300.00 1520.00 1630.00 1850.00 2100.0 2370.00 2360.00 2170.00 United Arab Emirates 583.0 580.00 588.0 565.00 559.00 577.0 638.0 578.000 611.00 588.00 608.00 659.00 718.0 1010.00 1310.00 1080.00 Argentina 368.0 357.00 373.0 383.00 400.00 382.0 367.0 121.000 148.00 174.00 217.00 259.00 334.0 437.00 488.00 405.00 - Make some changes to let the data shown in tidy form, with only one observation in each row to include the spending value for one country in a specific year.
country year spending Afghanistan 1994 NA Afghanistan 1995 NA Afghanistan 1996 NA Afghanistan 1997 NA Afghanistan 1998 NA Afghanistan 1999 NA - Let’s do some simple explorations here:
We can look for the spending value of all countries in 1994 and reorder them in increasing value. Also, we can fix the countries and obtain their spending conditions from 1994 to 2009. Using group_by function, we can calculate the average spending of government per year and analyze the trends. The average spending for each countries can also be analyzed. Here follows the four tables.
| country | year | spending |
|---|---|---|
| Congo, Dem. Rep. | 1994 | 0.19 |
| Myanmar | 1994 | 0.49 |
| Tajikistan | 1994 | 1.27 |
| Georgia | 1994 | 1.51 |
| Ethiopia | 1994 | 1.60 |
| Nepal | 1994 | 1.60 |
| country | year | spending |
|---|---|---|
| China | 1994 | 10.7 |
| China | 1996 | 13.7 |
| China | 1998 | 15.8 |
| China | 2000 | 16.8 |
| China | 2002 | 22.1 |
| China | 2004 | 31.2 |
| year | mean |
|---|---|
| 1994 | 308.3960 |
| 1995 | 317.2138 |
| 1996 | 305.1219 |
| 1997 | 306.8569 |
| 1998 | 318.5945 |
| 1999 | 310.2072 |
| country | mean |
|---|---|
| Afghanistan | 2.730333 |
| Albania | 52.018750 |
| Algeria | 75.437500 |
| Andorra | 1450.062500 |
| Angola | 42.845000 |
| Antigua and Barbuda | 311.687500 |
- We notice that there are some missing value in the data and we pick them out. For convinience, we just leave out the countries with missing value, thus we get the data of spending for 183 countries.
country year spending Afghanistan 1994 NA Afghanistan 1995 NA Afghanistan 1996 NA Afghanistan 1997 NA Afghanistan 1998 NA Afghanistan 1999 NA - The following data is the final spending data will we use to analyze with the expectancy data, including spending value of 183 countries.
country year spending Angola 1994 13.50 Angola 1995 8.72 Angola 1996 10.50 Angola 1997 6.65 Angola 1998 6.55 Angola 1999 12.50
2.Life expectancy
- Similarly, let us take a look at the expectancy data. It has expectancy data of 195 countries and includes data across 302 years from 1799 to 2099.
country 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 Afghanistan 28.2 28.2 28.2 28.2 28.2 28.2 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.5 27.5 27.5 27.5 27.5 27.5 27.7 27.9 28.1 28.3 28.5 28.7 28.9 29.1 29.3 29.4 29.6 29.8 30.0 30.2 30.4 30.6 30.8 31.0 31.2 31.4 31.6 31.8 32.0 32.2 32.4 32.5 32.7 32.9 33.1 33.3 33.5 33.7 33.9 34.1 34.3 34.5 34.7 34.9 35.0 35.2 35.4 35.6 35.8 36.0 36.2 36.4 36.6 9.59 36.9 37.1 37.3 37.5 37.7 37.9 38.1 38.3 38.4 38.6 38.8 39.0 39.2 39.4 39.6 39.8 39.9 40.1 40.3 40.5 40.7 40.9 41.0 41.2 41.4 41.6 41.8 42.0 42.2 42.4 42.5 42.7 42.9 43.1 43.5 43.3 43.9 44.1 44.3 44.5 44.7 45.0 45.3 45.5 45.7 45.9 46.1 46.3 46.5 46.7 46.9 47.1 47.3 47.3 47.3 47.4 47.5 47.7 47.9 46.4 44.7 43.7 44.3 44.1 42.3 39.9 42.0 43.3 45.9 48.5 52.7 53.8 53.8 54.2 54.4 53.9 54.3 54.7 54.5 53.3 54.7 54.7 54.8 55.5 56.5 57.1 57.6 58.0 58.5 59.2 59.9 60.5 61.0 61.4 61.9 61.9 61.9 62.0 62.9 62.7 63.3 63.4 64.0 64.3 64.6 64.9 65.2 65.4 65.7 66.0 66.2 66.4 66.6 66.9 67.1 67.3 67.5 67.7 67.9 68.0 68.2 68.4 68.6 68.8 68.9 69.1 69.2 69.4 69.5 69.7 69.8 70.0 70.2 70.3 70.4 70.6 70.7 70.9 71.0 71.1 71.3 71.4 71.5 71.7 71.8 72.0 72.1 72.2 72.3 72.5 72.6 72.7 72.9 73.0 73.1 73.3 73.4 73.5 73.7 73.8 74.0 74.1 74.2 74.3 74.5 74.6 74.8 74.9 75.0 75.2 75.3 75.4 75.5 75.7 75.8 76.0 76.1 76.2 76.4 76.5 76.6 76.8 Angola 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.2 27.4 27.6 27.7 27.9 28.1 28.3 28.5 28.7 28.9 29.1 29.3 29.4 29.6 29.8 30.0 30.2 30.4 30.6 30.8 30.9 31.1 31.3 31.5 31.7 31.9 32.1 32.3 32.5 32.6 32.8 33.0 33.2 33.4 33.6 33.8 34.0 34.2 34.4 34.5 34.7 34.9 35.1 35.3 35.5 35.7 35.9 13.90 36.2 36.4 36.6 36.8 37.0 37.2 37.4 37.6 37.8 37.9 38.1 38.3 38.5 38.7 38.9 39.1 39.3 39.5 39.6 39.8 40.0 40.2 40.7 41.3 41.8 42.3 42.9 43.4 43.9 44.5 45.0 45.6 45.6 45.6 45.6 45.6 45.5 45.7 45.8 45.9 46.1 46.3 44.8 45.0 45.2 45.4 45.6 45.8 46.0 46.2 46.4 46.6 46.8 47.0 47.2 47.4 47.5 47.5 47.7 47.8 48.0 48.1 48.2 48.2 48.2 48.4 48.6 48.6 48.6 48.6 49.4 49.7 50.3 50.3 49.0 50.3 51.2 51.7 51.6 50.6 51.9 52.8 53.4 54.5 55.1 55.5 56.4 57.0 58.0 58.8 59.5 60.2 60.8 61.4 62.1 63.0 63.5 63.9 64.2 64.6 65.1 65.2 65.8 66.1 66.4 66.8 67.1 67.4 67.7 68.0 68.3 68.6 68.9 69.2 69.4 69.7 70.0 70.2 70.5 70.7 70.9 71.1 71.4 71.6 71.8 72.0 72.2 72.4 72.5 72.7 72.9 73.1 73.3 73.4 73.6 73.8 73.9 74.1 74.2 74.4 74.5 74.7 74.8 75.0 75.1 75.3 75.4 75.5 75.7 75.8 76.0 76.1 76.2 76.4 76.5 76.6 76.8 76.9 77.0 77.2 77.3 77.4 77.5 77.7 77.8 77.9 78.0 78.2 78.3 78.4 78.6 78.7 78.8 79.0 79.1 79.2 79.3 79.5 79.6 79.7 79.9 80.0 Albania 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.3 35.3 35.3 35.3 35.3 35.3 35.2 35.2 35.2 35.2 35.2 35.2 35.1 35.1 35.1 35.1 35.1 35.1 35.0 35.0 35.0 35.0 35.0 35.0 35.0 34.9 34.9 34.9 34.9 34.9 34.9 34.8 34.8 34.8 34.8 34.8 34.8 34.7 34.7 34.7 34.7 34.7 34.7 34.6 19.00 34.6 34.6 34.6 34.6 34.5 34.5 34.5 34.5 34.5 34.5 34.5 35.3 36.1 37.0 37.9 38.7 39.6 40.4 41.3 42.1 41.6 40.7 40.1 38.7 35.8 32.9 45.4 48.3 49.7 50.5 51.4 52.2 53.6 54.5 55.4 56.1 56.3 58.0 59.3 61.0 61.7 62.5 63.3 63.3 63.8 64.4 64.8 65.5 66.1 66.5 67.1 67.8 68.3 68.8 69.3 69.8 70.2 70.7 71.1 71.7 71.3 71.3 71.3 71.4 71.2 71.4 71.9 72.3 72.2 72.4 72.5 72.8 72.6 73.2 73.8 74.6 74.6 74.5 72.9 74.8 75.1 75.4 76.0 75.9 75.6 75.8 76.2 76.9 77.5 77.6 78.0 78.1 78.1 78.2 78.3 78.2 78.1 78.2 78.3 78.4 78.5 77.9 78.7 78.8 79.0 79.1 79.2 79.4 79.5 79.7 79.8 80.0 80.2 80.3 80.5 80.7 80.8 80.9 81.1 81.2 81.3 81.5 81.6 81.8 81.9 82.0 82.2 82.3 82.4 82.6 82.7 82.8 83.0 83.1 83.3 83.4 83.5 83.7 83.8 83.9 84.0 84.1 84.3 84.4 84.5 84.6 84.7 84.8 84.9 85.0 85.2 85.3 85.4 85.5 85.6 85.7 85.8 85.9 86.0 86.1 86.2 86.3 86.4 86.5 86.6 86.7 86.8 86.9 87.0 87.1 87.2 87.3 87.4 87.5 87.6 87.7 87.8 87.9 88.0 88.2 88.3 88.4 Andorra NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 74.6 74.7 74.8 75.0 75.1 75.2 75.3 75.4 75.5 75.6 75.7 75.8 75.9 76.0 76.2 76.3 76.4 76.5 76.7 76.8 77.0 77.1 77.2 77.4 77.5 77.7 77.8 78.0 78.1 78.2 78.3 78.4 78.5 78.5 78.6 78.7 78.8 78.8 78.9 79.0 79.0 79.1 79.2 79.3 79.5 79.8 80.0 80.2 80.4 80.6 80.8 80.9 81.1 81.2 81.3 81.4 81.5 81.7 81.8 81.8 81.8 81.9 81.9 82.0 82.0 82.0 82.1 82.1 82.1 82.2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA United Arab Emirates 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.9 31.0 31.2 31.3 31.5 31.6 31.8 32.0 32.1 32.3 32.4 32.6 32.8 32.9 33.1 33.2 33.4 33.5 33.7 33.9 34.0 34.2 34.3 34.5 34.6 34.8 35.0 35.1 35.3 35.4 35.6 35.7 35.9 36.1 36.2 36.4 36.5 36.7 36.8 37.0 37.2 37.3 37.5 37.6 37.8 38.0 38.1 31.70 38.4 38.6 38.7 38.9 39.0 39.2 39.4 39.5 39.7 39.8 40.0 40.1 40.3 40.5 40.6 40.8 40.9 41.1 41.3 41.4 41.6 41.7 41.9 42.0 42.2 42.4 42.5 45.6 48.8 52.0 55.2 58.4 58.5 58.6 58.7 58.8 58.9 58.8 59.3 59.6 59.7 60.3 60.8 61.3 61.6 62.1 62.6 63.0 63.4 63.8 64.2 64.0 64.9 65.1 65.4 65.7 66.0 66.3 66.6 67.0 67.3 67.6 68.0 68.1 67.9 68.4 68.5 68.5 68.5 68.5 68.6 68.7 68.7 68.8 68.8 68.7 68.8 68.9 69.0 69.2 69.2 69.1 69.2 69.4 69.3 69.1 69.2 69.5 70.0 70.4 70.6 70.8 71.0 71.2 71.6 73.0 73.2 73.4 73.5 73.7 73.9 74.0 74.2 74.3 74.4 74.6 74.7 74.8 75.0 75.1 75.2 75.4 75.5 75.6 75.8 75.9 76.0 76.2 76.3 76.4 76.6 76.7 76.8 77.0 77.1 77.2 77.3 77.5 77.6 77.7 77.9 78.0 78.1 78.2 78.4 78.5 78.6 78.7 78.8 79.0 79.1 79.2 79.3 79.4 79.5 79.7 79.8 79.9 80.0 80.1 80.2 80.3 80.4 80.5 80.7 80.8 80.9 81.0 81.0 81.2 81.3 81.3 81.4 81.5 81.6 81.7 81.8 81.9 82.0 82.1 82.2 82.3 82.4 82.5 82.6 82.7 82.8 82.9 83.0 83.1 83.2 83.3 Argentina 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.2 33.3 33.3 33.3 33.3 33.2 33.2 33.2 33.1 33.0 33.0 32.9 32.8 33.1 33.4 33.7 34.0 34.3 34.1 34.0 33.9 33.8 33.6 34.3 35.1 35.8 36.5 37.2 37.8 38.4 39.0 39.6 40.2 41.0 41.8 42.6 43.4 44.2 44.7 45.3 45.9 46.4 47.0 47.8 48.7 42.50 50.3 51.2 51.7 52.2 52.7 53.2 53.7 54.1 54.4 54.8 55.2 55.5 55.6 55.6 55.6 55.6 55.6 56.6 57.7 58.7 59.7 60.7 61.3 61.9 62.5 63.2 63.8 63.7 63.6 63.5 63.4 63.3 63.5 64.2 64.1 64.7 64.5 65.2 65.2 65.4 65.4 65.3 65.7 65.8 65.8 65.8 66.1 66.6 66.5 66.0 65.9 66.1 66.9 67.3 67.7 67.9 68.0 67.0 67.7 69.0 69.8 70.2 70.3 70.9 70.7 70.8 71.7 72.0 72.1 72.1 72.3 72.5 72.7 72.8 73.0 73.4 73.4 73.5 73.6 73.7 73.8 74.2 74.3 74.3 74.4 74.9 75.3 75.4 75.3 75.7 75.8 75.9 76.0 76.2 76.3 76.5 76.5 76.2 76.3 76.5 76.6 74.6 76.9 77.0 77.2 77.3 77.5 77.6 77.8 77.9 78.0 78.2 78.3 78.5 78.6 78.8 78.9 79.1 79.2 79.4 79.5 79.7 79.8 80.0 80.1 80.3 80.4 80.6 80.7 80.8 81.0 81.2 81.3 81.4 81.6 81.7 81.9 82.0 82.2 82.3 82.4 82.6 82.7 82.8 83.0 83.1 83.2 83.4 83.5 83.6 83.7 83.9 84.0 84.1 84.2 84.3 84.5 84.6 84.7 84.8 84.9 85.0 85.1 85.3 85.4 85.5 85.6 85.7 85.8 85.9 86.0 86.1 86.2 86.3 86.5 86.5 86.7 86.8 86.9 87.0 87.1 87.2 - Make some changes to let the data shown in tidy form, with only one observation in each row to include the expectancy value for one country in a specific year.
country year expectancy Afghanistan 1799 28.2 Afghanistan 1800 28.2 Afghanistan 1801 28.2 Afghanistan 1802 28.2 Afghanistan 1803 28.2 Afghanistan 1804 28.2 - We notice that there are more countries recorded in the expectancy data and we merge these two data according to the spending data we just figured out. (There is no missing value in the expectancy data, which makes sense because it is a imaginary data. )
country year spending expectancy Albania 1994 13.9 74.6 Albania 1995 17.1 74.5 Albania 1996 14.2 72.9 Albania 1997 18.6 74.8 Albania 1998 28.1 75.1 Albania 1999 27.2 75.4
Plotting
1.Overall look
- From the graph, we can have a sense that higher government health spending per person will lead to higher life expectancy, which makes sense to us. In the real life, government health investment can be used to do research on health food to provide people with food with lower calories, fat and sugar. Also, reduced taxed on green food can also keep citizens healthier. Public infrastructure and activities from public park to city marathon also ensure people a healther life, which all increase people expectation for longevity.
2.Trends of spending and expectancy in one country
- Trends of spending and expectancy in China
- Trends of spending and expectancy in United States
- When comparing China and United States, we can see that there is a similar increase in life expectancy but the government health spending per person is much higher in the US. This can be partly explained by the large population in China or comparatively lower government spending on health, which is worth to be figured out.
3.Trends of spending and expectancy between ten countries
Put one country you are interested in and in ‘trends’ function and you will get the comparison of government health spending per person and life expectancy with other nine countries. Here we choose China and United States as examples.
We use set.seed when selecting other nine countries to be compared with both in part3 and part4 to make it more comparable.
China
United States
When comparing China and United Stated with other countries, we should pay more attention to the limit of y-axis even though the graphs are presented in the same height.
4. Relationship between spending and expectancy
Put one country you are interested in and in ‘rela()’ function and you will get the comparison of relationship between government health spending per person and life expectancy with other nine countries.
China
United States
